A network based spatial risk index indicator to guide active surveillance
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1
University of São Paulo, Brazil
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2
Department of Preventive Veterinary Medicine and Animal Health, Faculty of Veterinary Medicine and Animal Science, University of Sao Paulo, Brazil
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3
Secretariat of Agriculture, Livestock and Rural Development (Brazil), Brazil
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4
North Carolina State University, United States
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5
Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, United States
Background and specific objectives
Trade of infected, untested or false-negative animals have been associated major disease spread events (Firestone et al., 2012, 2019; VanderWaal et al., 2018; Machado et al., 2019). Movement data has been used extensively to represent dynamic and static connections between premises (Cárdenas et al., 2018; Kinsley et al., 2019), to further draw conclusions about: disease spread patters (Firestone et al., 2019; Kinsley et al., 2019), simulate and design improved disease control strategies (Mohr et al., 2018), etc.. However, the majority of these studies are based upon movement data from local, regional or national disease surveillance tracking systems (Cárdenas et al., 2018; Mohr et al., 2018), which are susceptible to a wide variety of data entry and completeness issues e.g. typos, missing or same origin and destination. The temptation to use this information to reconstruct networks and make surveillance decisions quickly, are often overlook or neglect the fundamental necessity of addressing data quality carefully. To overcome that, we propose a comprehensive data completeness step to identify and overcome the most common errors.
Substantial heterogeneity has been observed in many livestock-specific networks, often with a few farms responsible for a disproportionately large number of contacts (Shirley and Rushton, 2005), which are often selected to undergo active surveillance activities, e.g. additional blood sampling, movement restrictions. On the flipside, local or national surveillance strategic plans are built toward specific high-risk geographical areas, which makes target interventions at farms less attractive, especially in low-income countries.
Here, we use movement data of three species (cattle + small ruminants + swine) to reconstruct a municipality-level contact network. We map movement data completeness error at municipality-level and further used network metrics to map multispecies risk areas to facilitate active surveillance strategic plans in one Brazilian state.
Methods
Typology of movement data quality analysis
Cattle and buffaloes, swine and small ruminant’s movement data from 2015 until 2018 were obtained from the State of Rio Grande do Sul, Brazil. Altogether, we collected 1.621.236 individual batches between 269.390 farms and 497 municipalities. The data included farm locations, farms of origin and destination, date of movement, movement types (e.g., reproduction, sport). In evaluating movement data quality, we proposed error categories, which included: 1) consistency, 2) legibility and 3) completeness. Here we will focus only on data completeness. Completeness maps represent the frequency of movement data between municipalities with at least one-quality and usability issue divided by the total number of movements originated from each municipality.
Network based risk index
A multispecies network in which all species movement data were aggregated (cattle and buffaloes, swine and small ruminant) from 2015 until 2018 was reconstructed. Nodes were municipality while edges the batches. With the network on hand, we captured the following centrality measures: in-degree, out-degree, Page rank, cluster coefficient and betweenness. Finally, to propose a network based spatial risk index, we scaled all network metrics from “0” to “1” values, and then sum all to generate the raw risk index map. Furthermore, we also proposed a population-based index, which multiplies raw index by the exposed population in each municipality. An index of “1” describes a node (municipality) with the highest risk for the considered network.
Results
Among the main issues found in the data; same farm identification for farm of origin and destination (n=16.349 movements), missing or error in the geolocation (n=12.153 movements), missing dates, farm of origin, and farm of destination (n=534.317 movements). In Fig. 1 we show the municipalities’ absolute number movements with at least one of the above completeness records considering all species movement data together. Fig. 2 map the proportion of movement data removed due completeness matters over the total number of movements. Total movement data considered usable, were 1.604.418 movements, among 497 municipalities, in total 85.540.373 animals traded. We identified elevated completeness errors in the southern part of the state (Fig. 1-2). The municipality with highest completeness value had up to 21.7 % of the data removed; this municipality has a large population of cattle and small ruminants, which further pose great importance before the risk for the reintroduction of Foot-and-mouth disease (FMD).
We also identify municipalities with the greatest over all network relevance, by calculating the proposed spatial risk index (Fig. 3). Municipalities with index of “1” should have priority for active surveillance activities. High-risk municipalities were located in areas with the likelihood for FMD introduction are the greatest (Dos Santos et al., 2017). A direct consequence of such catastrophic event would potentially spread FMD the larger number of municipalities more rapidly than if introduced into any other municipality.
Conclusions
The finding in this study point to a neglected pre-process step prevalent in the majority of network analysis studies, the completeness in movement data. The municipalities in the study region were intensely connected; therefore, the use of the network analysis allowed identifying key municipalities that play an important role in the containment and or propagation of any infectious disease. The proposed network-based risk index identified municipalities, which should be first while active surveillance is planned.
The authors declare that there are no conflict of interests
Acknowledgements
This project was funded by the Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University-CVM Global Health.
References
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Dos Santos, D.V., G. Sousa e Silva, E.J. Weber, H. Hasenack, F.H.S. Groff, B. Todeschini, M.R. Borba, A.A.R. Medeiros, V.B. Leotti, C.W. Canal, and L.G. Corbellini, 2017: Identification of foot and mouth disease risk areas using a multi-criteria analysis approach. PLoS OneDOI: 10.1371/journal.pone.0178464.
Firestone, S.M., R.M. Christley, M.P. Ward, and N.K. Dhand, 2012: Adding the spatial dimension to the social network analysis of an epidemic: Investigation of the 2007 outbreak of equine influenza in Australia. Prev. Vet. Med.DOI: 10.1016/j.prevetmed.2012.01.020.
Firestone, S.M., Y. Hayama, R. Bradhurst, T. Yamamoto, T. Tsutsui, and M.A. Stevenson, 2019: Reconstructing foot-and-mouth disease outbreaks: a methods comparison of transmission network models. Sci. Rep.DOI: 10.1038/s41598-019-41103-6.
Kinsley, A.C., A.M. Perez, M.E. Craft, and K.L. Vanderwaal, 2019: Characterization of swine movements in the United States and implications for disease control. Prev. Vet. Med.DOI: 10.1016/j.prevetmed.2019.01.001.
Machado, G., C. Vilalta, M. Recamonde-Mendoza, C. Corzo, M. Torremorell, A. Perez, and K. VanderWaal, 2019: Identifying outbreaks of Porcine Epidemic Diarrhea virus through animal movements and spatial neighborhoods. Sci. Rep. in press, DOI: DOI:10.1038/s41598-018-36934-8.
Mohr, S., M. Deason, M. Churakov, T. Doherty, and R.R. Kao, 2018: Manipulation of contact network structure and the impact on foot-and-mouth disease transmission. Prev. Vet. Med.DOI: 10.1016/j.prevetmed.2018.05.006.
Shirley, M.D.F., and S.P. Rushton, 2005: The impacts of network topology on disease spread. Ecol. Complex.DOI: 10.1016/j.ecocom.2005.04.005.
VanderWaal, K., A. Perez, M. Torremorrell, R.M. Morrison, and M. Craft, 2018: Role of animal movement and indirect contact among farms in transmission of porcine epidemic diarrhea virus. Epidemics 24, 67–75, DOI: 10.1016/j.epidem.2018.04.001.
Keywords:
risk index,
spatial analysis,
Multispecies movement data,
Movement data completeness error,
spatial
Conference:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.
Presentation Type:
Regular oral presentation
Topic:
Spatial-explicit or spatio-temporal network analysis
Citation:
Cespedes Cardenas
N,
Grisi-Filho
JH,
Ardila Galvis
JO,
Lopes
FP,
Medeiros
AA and
Machado
G
(2019). A network based spatial risk index indicator to guide active surveillance.
Front. Vet. Sci.
Conference Abstract:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data.
doi: 10.3389/conf.fvets.2019.05.00030
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Received:
10 Jun 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Dr. Nicolas Cespedes Cardenas, University of São Paulo, São Paulo, São Paulo, 05508-010, Brazil, ncesped@ncsu.edu
Prof. Gustavo Machado, North Carolina State University, Raleigh, United States, gmachad@ncsu.edu